Data mining is the automatic discovery of statistically interesting and potentially useful patterns from large amounts of data. The goal of the course is to study the main methods used today for data mining and on-line analytical processing. Topics include Data Mining Architecture; Data Preprocessing; Mining Association Rules; Classification; Clustering; On-Line Analytical Processing (OLAP); Data Mining Systems and Languages; Advanced Data Mining (Web, Spatial, and Temporal data).
The course will study some advanced topics and techniques in database systems, with a focus on the aspects of database systems design & algorithms and big data processing for structured data. Traditional topics include query optimization, physical database design, transaction management, crash recovery, parallel databases. The course will also survey some the recent developments in selected areas such as NoSQL databases and SQL-based big data management systems for relational (structured) data.
This course will introduce selected advanced computational methods and apply them to problems in data analysis and relevant applications.
^ Each course normally carries 6 credits.
Only applicable to 2026-27 or thereafter intake.